Software Specialist at PTC R&D with 4+ years shipping enterprise‑scale Java, Angular, Python and Azure systems. CI/CD automation, cloud migration, and full‑stack PLM development.
tip: watch the terminal → a live demo of what I work on.
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I'm a Software Engineer with deep experience across the modern enterprise stack — from JVM backends and Angular frontends to Azure infrastructure, GitLab pipelines, and Salt/Terraform automation.
Currently shipping cloud‑migration tooling and PLM features at PTC R&D. Previously worked on identity & SSO at MiniOrange and built NLP scoring systems as an Analytics Intern.
4+ years across enterprise R&D, identity, and analytics teams.
A mix of full‑stack apps, algorithm visualizers, computer‑vision experiments, and embedded systems.
Specialized monitoring tool during migration project, submitting malformed packages would stall CI/CD pipeline for 22+ hours. It inspect, validate and correct package content on-demand before pipeline execution.
[ Impact reduce long-running pipeline failures, minimized engineering idle time, and saved multiple pipeline processing days by preventing late-stage execution breakdowns. ]
Visualizes 4+ sorting algorithms (Bubble, Quick, Insertion, Selection) with adjustable speed and array size.
Race simulator using fuzzy‑logic inputs and max‑membership defuzzification — built without Python's fuzzy module.
Two algorithms — backtracking and constraint propagation without backtracking — with performance comparison.
Voice‑interactive assistant with OS‑level access — file ops, app launching, and shell commands.
Real‑time facial landmark detection (68 points, iBUG dataset) overlaying digital masks.
Eye Aspect Ratio (EAR) tracked in real time to trigger drowsiness alerts — safety‑focused CV.
Event website built with Bootstrap on a local Django server with ORM persistence.
Op‑amp + thermistor circuit forming a heat‑detecting fire alarm. Hands‑on PCB design.
8051 microcontroller programmed to automate street‑light switching by time.
Comfortable across the full stack — backend, frontend, infra, and ML foundations.
Stanford Online — regression, gradient descent, classification, overfitting.
Neural networks, back‑propagation, regularization, bias–variance.
K‑means, PCA, recommender systems, MDPs, Q‑learning.
Two‑day hands‑on build on the KK2.1.5 board with PID tuning.
I'm open to full‑time roles, freelance contracts, and interesting collaborations. The best way to reach me is over email or LinkedIn.
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